Anomalous event detection using a semi-two dimensional hidden Markov model

Nallaivarothayan, Hajananth, Ryan, David, Denman, Simon, Sridharan, Sridha, & Fookes, Clinton B. (2012) Anomalous event detection using a semi-two dimensional hidden Markov model. In Proceedings of the 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA 12), IEEE Computer Society, Fremantle, Western Australia, pp. 1-7.

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The rapid increase in the deployment of CCTV systems has led to a greater demand for algorithms that are able to process incoming video feeds. These algorithms are designed to extract information of interest for human operators. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned `normal' model. Many researchers have tried various sets of features to train different learning models to detect abnormal behaviour in video footage. In this work we propose using a Semi-2D Hidden Markov Model (HMM) to model the normal activities of people. The outliers of the model with insufficient likelihood are identified as abnormal activities. Our Semi-2D HMM is designed to model both the temporal and spatial causalities of the crowd behaviour by assuming the current state of the Hidden Markov Model depends not only on the previous state in the temporal direction, but also on the previous states of the adjacent spatial locations. Two different HMMs are trained to model both the vertical and horizontal spatial causal information. Location features, flow features and optical flow textures are used as the features for the model. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

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ID Code: 56605
Item Type: Conference Paper
Refereed: Yes
Keywords: Event detection, Feature extraction, Hidden Markov models, Optical imaging, Training, Trajectory, Vectors
DOI: 10.1109/DICTA.2012.6411711
ISBN: 9781467321815
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 IEEE Inc. All rights reserved.
Copyright Statement: Copyright and Reprint Permissions Abstracting is permitted with credit to the source. Libraries are permitted to photocopy beyond the limit of U.S. copyright law for private use of patrons those articles in this volume that carry a code at the bottom of the first page, provided the per-copy fee indicated in the code is paid through Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923.
Deposited On: 22 Jan 2013 23:18
Last Modified: 26 Jun 2015 07:36

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